Search Results (540)

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the Double Moving Average (DMA), Double Exponential Smoothing (DES), and Holt–Winters methods (both additive and multiplicative trends), as well as long short-term memory (LSTM) recurrent neural networks. Our proposed hybrid model builds upon previous work with improvements in hyperparameter tuning using the GRG nonlinear optimization method. The results demonstrate that the hybrid LSTM-DES models outperformed all individual models in terms of both accuracy and demand variation. The reason behind the success of the hybrid model is that it works well with both linear and nonlinear trends, as well as the seasonality of certain periods. Furthermore, the forecast results for train passengers will serve as input variables to estimate the future revenue of train travel programs in various regions, including rail tourism. This information will help identify which regions should receive increased focus and investment by the train tourism program. For example, if the forecasted number of passengers in the northern region is high, the State Railway of Thailand will promote and improve infrastructure at the train station and nearby tourist attractions. Full article

Accurate forecasting of COVID-19 case numbers is critical for timely and effective public health interventions. However, epidemiological data’s irregular and noisy nature often undermines the predictive performance. This study examines the influence of four smoothing techniques—the rolling mean, the exponentially weighted moving average, a Kalman filter, and seasonal–trend decomposition using Loess (STL)—on the forecasting accuracy of four models: LSTM, the Temporal Fusion Transformer (TFT), XGBoost, and LightGBM. Weekly case data from Ukraine, Bulgaria, Slovenia, and Greece were used to assess the models’ performance over short- (3-month) and medium-term (6-month) horizons. The results demonstrate that smoothing enhanced the models’ stability, particularly for neural architectures, and the model selection emerged as the primary driver of predictive accuracy. The LSTM and TFT models, when paired with STL or the rolling mean, outperformed the others in their short-term forecasts, while XGBoost exhibited greater robustness over longer horizons in selected countries. An ANOVA confirmed the statistically significant influence of the model type on the MAPE (p = 0.008), whereas the smoothing method alone showed no significant effect. These findings offer practical guidance for designing context-specific forecasting pipelines adapted to epidemic dynamics and variations in data quality. Full article

With the widespread application of unmanned aerial vehicles (UAVs), the safety detection system of UAVs has created an urgent need for anomaly detection technology. As a direct representation of system health status, flight data contain critical status information, driving data-driven methods to gradually replace traditional dynamic modeling as the mainstream paradigm. The former effectively circumvent the problems of nonlinear coupling and parameter uncertainty in complex dynamic modeling. However, data-driven methods still face two major challenges: the scarcity of anomalous flight data and the difficulty in extracting strong spatio-temporal coupling among flight parameters. To address these challenges, we propose an unsupervised anomaly detection method based on the causality-enhanced graph neural network (CEG). CEG innovatively introduces a causality model among flight parameters, achieving targeted extraction of spatial features through a causality-enhanced graph attention mechanism. Furthermore, CEG incorporates a trend-decomposed temporal feature extraction module to capture temporal dependencies in high-dimensional flight data. A low-rank regularization training paradigm is designed for CEG, and a residual adaptive bidirectional smoothing strategy is employed to eliminate the influence of noise. Experimental results on the ALFA dataset demonstrate that CEG outperforms state-of-the-art methods in terms of Precision, Recall, and F1 score. The proposed method enables accurate and robust anomaly detection on a wide range of anomaly types such as engines, rudders, and ailerons, validating its effectiveness in handling the unique challenges of UAV anomaly detection. Full article

Human capital is a key element necessary for the smooth operation of an organization based on sustainable development. It is not only important for building strategy but also affects the performance of day-to-day operations. Managers must constantly monitor the changes taking place around the organization and make quick decisions in line with sustainability. They enable the organization to adapt to current market conditions and meet closed-loop requirements. These solutions are an important issue in forest management organizations. Considering the expanded mission of forests, it is clear that the role of forests today is much broader than just protecting biodiversity. Forestry institutions need adequate staff and human resources to effectively carry out forest management tasks and properly analyze trends and patterns of the sustainable use of forest resources. The purpose of the article was to analyze and evaluate human capital through its commitment to the organization’s tasks within the framework of sustainability. The research involved a sample for employees working in a selected unit of the State Forest Holding in Poland. The research was based on an anonymous employee survey on job engagement. The following aspects were assessed, commitment to the organization, sense of responsibility to the organization, interest in the work, and willingness to make sacrifices for the company in order to meet the demands of a modern forestry company oriented to the requirements of the new forestry strategy. Our reanalysis showed that gender and job type do not have a significant impact on commitment. However, an employee’s age and length of service do influence the behaviour and commitment of state forestry employees. Full article

In this work, arc ion plating (AIP) was employed to deposit AlCrMoN coatings on cemented carbide substrates, and the effects of substrate bias voltages (10 V, −100 V, −120 V, and −140 V) on the microstructures, mechanical properties, and tribological behaviors of the coatings were investigated. The results showed that all AlCrMoN coatings exhibited a single-phase face-centered cubic (FCC) structure with columnar crystal growth and excellent adhesion to the substrate. As the negative bias voltage increased, the grain size of the coatings first decreased and then increased, while the hardness and elastic modulus showed a trend of first increasing and then decreasing, with the maximum hardness reaching 36.2 ± 1.33 GPa. Room-temperature ball-on-disk wear tests revealed that all four coatings demonstrated favorable wear resistance. The coating deposited at −100 V exhibited the lowest average friction coefficient of 0.47 ± 0.02 and wear rate ((3.27 ± 0.10) × 10⁻⁸ mm³/(N∙m)), featuring a smooth wear track with minimal oxide debris. During the steady-state wear stage, the dominant wear mechanisms of the AlCrMoN coatings were identified as oxidative wear combined with abrasive wear. Full article

The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing challenges for the long-term analysis of atmospheric pollution trends, responses to sudden ecological events, and disaster management. This study aims to develop a high-precision method to fill spatial AOD missing values and generate daily full-coverage AOD products for the Beijing–Tianjin–Hebei region in 2021 by integrating multi-dimensional data, including meteorological models, multi-source remote sensing, surface conditions, and nighttime light parameters, and applying machine learning methods. A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R²) of 0.93. Seasonal evaluation further indicated that the model’s simulation was best in winter. Variable importance analysis identified relative humidity (RH) as the most critical factor influencing model results. The reconstructed full-coverage AOD product exhibited a spatial distribution trend of significantly higher values in the southern plain areas compared to mountainous regions, consistent with the actual aerosol distribution patterns in the Beijing–Tianjin–Hebei area. Moreover, the product demonstrated overall smoothness and high accuracy. This research lays the foundation for establishing a long-term, 1 km resolution, daily spatially continuous AOD product for the Beijing–Tianjin–Hebei region and beyond, providing more robust data support for addressing regional and larger-scale environmental challenges. Full article

Pipeline steel is highly susceptible to hydrogen embrittlement (HE) in hydrogen environments, which compromises its structural integrity and operational safety. Existing studies have primarily focused on the degradation trends of mechanical properties in hydrogen environments, but there remains a lack of quantitative failure prediction models. To investigate the failure behavior of X65 pipeline steel under hydrogen environments, this paper utilized notched round bar specimens with three different radii and smooth round bar specimens to examine the effects of pre-charging time, the coupled influence of stress triaxiality and hydrogen concentration, and the coupled influence of strain rate and hydrogen concentration on the HE sensitivity of X65 pipeline steel. Fracture surface morphologies were characterized using scanning electron microscopy (SEM), revealing that hydrogen-enhanced localized plasticity (HELP) dominates failure mechanisms at low hydrogen concentrations, while hydrogen-enhanced decohesion (HEDE) becomes dominant at high hydrogen concentrations. The results demonstrate that increasing stress triaxiality or decreasing strain rate significantly intensifies the HE sensitivity of X65 pipeline steel. Based on the experimental findings, failure prediction models for X65 pipeline steel were developed under the coupled effects of hydrogen concentration and stress triaxiality as well as hydrogen concentration and strain rate, providing theoretical support and mathematical models for the engineering application of X65 pipeline steel in hydrogen environments. Full article

The ability to predict the volume of methane outbursts in coal mines is critical for the prevention of methane outburst accidents and the assurance of coal-mine safety. This paper’s central argument is that existing prediction models are limited in several ways. These limitations include the complexity of the models and their poor ability to generalize. The paper proposes a methane outburst volume-prediction and early-warning method. This method is based on a secondary decomposition and improved TSMixer model. First, data smoothing is achieved through an STL decomposition–adaptive Savitzky–Golay filtering–reconstruction framework to reduce temporal complexity. Second, a CEEMDAN-Kmeans-VMD secondary decomposition strategy is adopted to integrate intrinsic mode functions (IMFs) using K-means clustering. Variational mode decomposition (VMD) parameters are optimized via a novel exponential triangular optimization (ETO) algorithm to extract multi-scale features. Additionally, a refined TSMixer model is proposed, integrating reversible instance normalization (RevIn) to bolster the model’s generalizability and employing ETO to fine-tune model hyperparameters. This approach enables multi-component joint modeling, thereby averting error accumulation. The experimental results demonstrate that the enhanced model attains RMSE, MAE, and R² values of 0.0151, 0.0117, and 0.9878 on the test set, respectively, thereby exhibiting a substantial improvement in performance when compared to the reference models. Furthermore, we propose an anomaly detection framework based on STL decomposition and dual lonely forests. This framework improves sensitivity to sudden feature changes and detection robustness through a weighted fusion strategy of global trends and residual anomalies. This method provides efficient and reliable dynamic early-warning technology support for coal-mine gas disaster prevention and control, demonstrating significant engineering application value. Full article

The durability of fuel cells is the main obstacle to their large-scale application. Deep learning-based methods improve the accuracy of fuel cell lifespan degradation prediction. However, their reliance on static health indicators and application in bench experiment environments limits their ability to capture degradation trends under dynamic conditions. This paper proposes a novel lifespan degradation prediction method for fuel cells operating in real-world traffic environments, utilizing Relative Voltage Loss Rate (RVLR) as the health indicator. Initially, fuel cell lifespan degradation data with varying characteristics are obtained through a dynamic bench experiment and two sets of road driving experiments. Subsequently, a lifespan degradation prediction model based on the Bidirectional Long Short-Term Memory Dual-Attention Transformer (BL-DATransformer) is proposed. An ablation study is conducted on this architecture, with analysis performed to evaluate the influence of diverse input features on model performance. Finally, the comparison results with LSTM, Transformer, and Informer indicate that under smooth traffic conditions, when the training length is 70%, the RMSE is reduced by 84.32%, 74.94%, and 18.49%, respectively. Under congested traffic conditions, with the same training length, the RMSE is reduced by 88.30%, 78.33%, and 26.52%, respectively. The result demonstrates that the prediction method has high accuracy and practical application value. Full article

The aim of the homogenization of climatic time series is to remove non-climatic biases from the observed data, which are caused by technical or environmental changes during the period of observations. This bias removal is generally more successful for long-term trends and annual means than for monthly and daily values. The homogenization of probability distribution (HPD) may improve data accuracy even for daily data when the signal-to-noise ratio favors its application. HPD can be performed by quantile matching or spatial interpolations, but both of them have drawbacks. This study presents a new algorithm which helps to increase homogenization accuracy in all temporal and spatial scales. The new method is similar to quantile matching, but section mean values of the probability distribution function (PDF) are compared instead of individual daily values. The input dataset of the algorithm is identical with the homogenization results for section means of the studied time series. The algorithm decides about statistical significance for each break detected during the homogenization of the section means, and skips the insignificant breaks. Correction terms for removing the inhomogeneity biases of PDF are calculated jointly by a Benova-like equation system, a low pass filter is used for smoothing the prime results, and the mean value of the input time series between two consecutive detected breaks is preserved for each of such sections. This initial version does not deal with seasonal variations either during HPD or in other steps of the homogenization. The method has been tested connecting HPD to ACMANTv5.3, and using overall 8 wind speed and relative humidity datasets of the benchmark of European project INDECIS. The results show 4 to 12 percent RMSE reduction by HPD in all temporal scales, except for the extreme tails where a part of the results are weaker. Full article

The global environment has been facing sustainability threats recently owing to industrial and economic expansion. Hence, achieving the goals of carbon peak and carbon neutrality is crucial for promoting sustainable economic growth. To help the transportation industry achieve these goals, this study selects eight variables, including population size, per capita GDP, personal vehicle ownership, passenger and freight turnover, and green space coverage, as factors influencing the carbon emissions of the transportation industry in Jiangsu Province. This research uses these variables as the basis for predicting and analyzing transportation carbon emission trends from 2000 to 2021. In addition, the current study forecasts the future carbon emissions of the transportation industry and estimates the time of carbon emission peak in Jiangsu Province. To verify the accuracy of the results, this study compares the predicted results with those from other models. The whale optimization algorithm–support vector machine model is found to have the fewest errors among several models. On this basis, targeted measures are proposed to accelerate the carbon peak process and ensure the smooth achievement of carbon neutrality goals in Jiangsu Province. Results indicate that under the current policy measures, peak carbon emissions in Jiangsu Province will occur in 2038, with a peak of 48.72 million tons. Jiangsu Province should actively adopt energy-saving and emission-reduction measures, build a green and low-carbon transportation development model, and achieve the carbon peak target ahead of schedule. Findings from this study will provide valuable insights and practical recommendations for policy makers and stakeholders to formulate effective strategies for carbon reduction in the transportation sector, contributing to the sustainable development of China and the world. Full article

Post-operative feedback from hip replacement surgeries indicates that implanted ceramic artificial hip joints may produce abnormal noises during movement. This occurrence of joint noise is highly correlated with insufficient lubrication of ceramic-on-ceramic (COC) prostheses. Studies have shown that surface texture design can improve lubrication performance. In this study, the elastohydrodynamic lubrication model was established with designing textures on the surface of the COC hip joint, using Matlab R2018b and GNU FORTRAN in Codeblocks 20.03 programming. Iterative calculations were performed to determine the average bearing capacity of the oil film and the friction coefficient. The study explored the impact of texture parameters, including the aspect ratio and density, on the lubrication and friction performance of the hip joints. The results indicate that the textured surface generally has a higher fluid film bearing capacity by 161.5~637.7% and a lower friction coefficient by 10.7~60% than the smooth surface. The average bearing capacity of the fluid film increases with an increasing texture aspect ratio, while the trend of the friction coefficient is identical to the average bearing capacity results. As the texture density increases, the average bearing capacity of the fluid film first decreases and then increases, and the trend of the friction coefficient also increases accordingly. Among the nine design groups ($Sp=0.05,0.15,0.35,\epsilon =0.075,0.1,0.15$), based on the fuzzy comprehensive evaluation, the local optimal solution is $Sp=0.15$, $\epsilon =0.075$ for lubrication and wear resistance. Full article

In this paper, a panel smooth transition regression model is used to examine the nonlinear effects of financial globalization on energy security. These effects are examined in 123 countries for the period of 2000–2018. Control variables are armed forces, industrialization rate, trade value share, and urbanization rate, and the conversion variable is the financial globalization index in the following year. The results of the financial globalization effects can be obtained from both time and space. The results show that financial globalization has a positive nonlinear effect on energy security. When the logarithm of financial globalization in the previous year exceeds 0.0467, the coefficient between financial globalization and energy security will decrease from 0.0467 to 0.0209. Temporal variation analyses show that the positive effect followed a “decrease, increase, decrease” trend between 2000 and 2018. Spatial variation analyses show that the positive effect is greatest in Oceania and the Americas (with an effect coefficient of 0.0467) and smallest in Europe (with an effect coefficient of 0.0391). According to the results of the regional heterogeneity research, the Organization of the Petroleum Exporting Countries (OPEC) countries see a stronger nonlinear impact of financial globalization on energy security than non-OPEC countries. Full article

Faced with the challenges of the energy crisis and the need to reduce greenhouse gas emissions, Poland needs to increase the share of renewable energy sources in the energy mix. Development trends in the waste-to-energy market reflect the global energy transition. Poland generates about 13 million tonnes of municipal waste annually, a significant percentage of which is biodegradable waste that should be converted into biogas or used in thermal processes to produce electricity and heat. Despite the benefits of recovering energy from waste, there are technological, economic, and regulatory barriers that limit the development of this sector in Poland. Creating an efficient waste management system is one of the most important challenges today in terms of energy, the environment, and the economy. The circular economy is a fundamental element of the European Union’s environmental policy, including the European Green Deal, the main objective of which is to combat the carbon footprint. The amount of energy produced is decisively influenced by the structure of the deposited waste and the share of the calorific fraction in the total mass of municipal waste. This study aimed to develop forecasts for biodegradable municipal waste, using the simulation and optimisation of the exponential Brownian smoothing constant, and to estimate the value of recovered energy. The forecasts were based on data on selective waste collection from different provinces of Poland. The study reveals that the forecast for biodegradable municipal waste in the coming years shows an increasing trend, amounting to 2,696,500 tonnes in 2030, which will allow for a significant increase in energy recovery. Full article

The tribological behavior of molybdenum disulfide (MoS₂) coatings was systematically investigated under various controlled gas environments in a vacuum chamber. A hemispherical steel pin was slid cyclically over a MoS₂-coated steel disk, prepared via high-speed powder spraying. The study measured both dynamic and static friction coefficients under different gaseous atmospheres, including high vacuum, helium, argon, dry air, and water vapor. In high vacuum (10⁻⁵ Pa), an ultra-low dynamic friction coefficient (µ ≈ 0.01) was observed, while increasing values were recorded with helium (µ ≈ 0.03), argon (µ ≈ 0.04), dry air (µ ≈ 0.17), and water vapor (µ ≈ 0.30). Static friction coefficients followed a similar trend, decreasing significantly upon evacuation of water vapor or injection of inert gases. Surface analyses revealed that friction in vacuum or inert gases promoted smooth wear tracks and basal plane alignment of MoS₂ crystallites, while exposure to water vapor led to rougher, more disordered wear surfaces. Mass spectrometry and energetic modeling of physisorption interactions provided further insights into gas–solid interfacial mechanisms. These results demonstrate that the tribological performance of MoS₂ coatings is highly sensitive to the surrounding gas environment, with inert and vacuum conditions favoring low friction through enhanced basal plane orientation and minimal gas–surface interactions. In contrast, water vapor disrupts this structure, increasing friction and surface degradation. Understanding these interactions is crucial for optimizing MoS₂-based lubrication systems in varying atmospheric or sealed environments. Full article